Evolution Characteristics of Extreme Precipitation in Northern China: A Case Study of Chaohu Lake Basin
DOI:
https://doi.org/10.70917/jcc-2026-019Keywords:
Chaohu Lake Basin, extreme precipitation, mutation diagnosis, probability distributionAbstract
Under the background of global climate change, extreme precipitation events are becoming more frequent in the monsoon region of northern China. The Chaohu Lake Basin, as a typical transitional zone in the middle and lower reaches of the Yangtze River, has a precipitation variability that is simultaneously influenced by the interaction between the ocean and the atmosphere and the expansion of the Hefei metropolitan area. Based on daily precipitation data from 15 meteorological stations, this study aims to investigate the spatiotemporal patterns and drivers of extreme precipitation in the basin from 1961 to 2019 by constructing a "Trend-Mutation-Probability (TMP)" three-dimensional analysis framework. Results show: 1) The annual average precipitation in the Chaohu Lake Basin shows a significant upward trend (2.4 mm/yr, p < 0.05), with a more significant increase at the edge stations (such as station 15 reaching 5.1 mm/yr); 2) The year of the precipitation mutation point is 1979 (H = 0.52), and after the mutation, the 90th percentile (P=0.9) precipitation increases by 154.6 mm; 3) The PRCPTOT index shows the most significant growth (2.4 mm/yr), and the frequency of extreme events significantly increases. This study not only presents the novel TMP diagnostic framework for broader application but also delivers specific, actionable insights for updating flood risk management policies and infrastructure design standards in the densely populated Yangtze River Delta, thereby strengthening the region's climate resilience.
References
Ayugi B, Dike V, Ngoma H, et al. Future changes in precipitation extremes over East Africa based on CMIP6 models[J]. Water, 2021, 13(17): 2358. https://doi.org/10.3390/w13172358
Du H, Donat MG, Zong S, et al. Extreme Precipitation on Consecutive Days Occurs More Often in a Warming Climate[J]. Bulletin of the American Meteorological Society,2022, 103(4):1130-1145. https://doi.org/10.1175/BAMS-D-21-0140.1
Du Junkai, Qiu Yaqin, Du Junkai, et al. Evolution characteristics of the interannual and intra-annual precipitation in China from 1956 to 2016 [J]. Advances in Water Science, 2023, 34(2):182-196. https://dx.doi.org/10.14042/j.cnki.32.1309.2023.02.003
Franzke CLE. Changing temporal volatility of precipitation extremes due to global warming[J]. International Journal of Climatology,2022,42(16):8971-8983. https://doi.org/10.1002/joc.7789
Guo J, Shen Y, Wang X, et al. Evaluation and projection of precipitation extremes under 1.5°C and 2.0°C GWLs over China using bias-corrected CMIP6 models[J]. Iscience, 2023, 26(4):106179. https://doi.org/10.1016/j.isci.2023.106179
He Jia, Su Xiao. Research Progress on Extreme Climate Events and Evolution of Major Disasters [J]. Disaster science,2018,33(04):223-228. https://doi.org/10.3969/j.issn.1000-811X.2018.04.038
IPCC. Climate Change 2022: impacts, adaptation, and vulnerability[M]. Cambridge: Cambridge University Press, 2022. https://doi.org/10.1017/9781009325844
Koycegiz C. Spatiotemporal analysis of precipitation variability in an endorheic basin of Turkey with coordinated regional climate downscaling experiment data[J]. Alexandria Engineering Journal, 2024, 91: 368-381. https://doi.org/10.1016/j.aej.2024.02.010
Liu S, Huang S, Huang Q, et al. Identification of the non-stationarity of extreme precipitation events and correlations with large-scale ocean-atmospheric circulation patterns: A case study in the Wei River Basin, China[J]. Journal of Hydrology, 2017, 548:184-195. https://doi.org/10.1016/j.jhydrol.2017.03.012
Niu K, Hu Q, Wang Y, et al. Analysis on the Variation of Hydro-Meteorological Variables in the Yongding River Mountain Area Driven by Multiple Factors[J]. Remote Sensing, 2021, 13(16): 3199. https://doi.org/10.3390/rs13163199
Tang Q. Global change hydrology: Terrestrial water cycle and global change[J]. Science China-Earth Sciences,2020,63(3):459-462. https://doi.org/10.1007/s11430-019-9559-9
Teegavarapu R, Nayak A. Evaluation of long-term trends in extreme precipitation: Implications of in-filled historical data use for analysis[J]. Journal of Hydrology, 2017, 550. https://doi.org/10.1016/j.jhydrol.2017.05.030
Tuğrul T, Hinis M A. Trend analysis of hydrological and meteorological drought in Apa Dam, Türkiye[J]. Environmental Earth Sciences, 2024, 83(17): 502. https://doi.org/10.1007/s12665-024-11791-z
Tysa S K, Ren G, Zhang P, et al. Impact of urbanization on regional extreme precipitation trends observed at China national station network[J]. Weather and Climate Extremes, 2025, 48: 100760. https://doi.org/10.1016/j.wace.2025.100760
Wu F, Yang X, Cui Z, et al. The impact of human activities on blue-green water resources and quantification of water resource scarcity in the Yangtze River Basin[J]. Science of the Total Environment, 2024, 909: 168550. https://doi.org/10.1016/j.scitotenv.2023.168550
Xing S, Li X. Diurnal variation of global precipitation: an analysis of CMORPH data[J]. Journal of Tropical Meteorology,2019,25(1):45-53. https://doi.org/10.16555/j.1006-8775.2019.01.005
Xu Xiaolu, Zhang Fangmin, Deng Hanqing, et al. Changes in Precipitation Grades in China from 1961-2020 [J]. Research of Soil and Water Conservation,2024,31(2):181-189. https://doi.org/10.13869/j.cnki.rswc.2024.02.003
Zhang Cunjie, Xiao Chen, Li Shuai, et al. Construction of multi-extreme climate events composite grads index and comprehensive analysis of extreme climate in the Yangtze River Basin from 1961 to 2020 [J]. Journal of Geophysics,2023,66(3):920-938. https://doi.org/10.6038/cjg2022Q0255
Zhang S, Xu Q, Riffat S. Multi-objective optimization on sustainable retrofit of historical buildings using NSGA-II Algorithm: A case study of Suzhou[J]. Green Technology & Innovation, 2025: 7658. https://dx.doi.org/10.36922/gti.7658
Zhang W, Liu L, Li X, et al. Intensified response of extreme precipitation to rising temperature over the Tibetan Plateau from CMIP6 multi-model ensembles[J]. Journal of Hydrology, 2024, 637: 131397. https://doi.org/10.1016/j.jhydrol.2024.131397
Zhang Y, Wang K. Global precipitation system scale increased from 2001 to 2020[J]. Journal of Hydrology,2023,616:128768. https://doi.org/10.1016/j.jhydrol.2022.128768
Zhao L, Li L, Li Y, et al. Global extreme precipitation characteristics: the perspective of climate and large river basins[J]. Climate Dynamics,2024,62(2):1013-1030. https://doi.org/10.1007/s00382-023-06961-x
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Copyright (c) 2026 Ye Zhang, Leizhi Wang, Dan Tian , Wei ZHU, Lei Wu, Pengxin Deng, Jianwen Hu (Author)
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